wigest: a ubiquitous wifi-based gesture recognition system · wigest: a ubiquitous wifi-based...

WiGest: A Ubiquitous WiFi-based Gesture Recognition System

Heba Abdelnasser†, Moustafa Youssef‡, and Khaled A. Harras*Alexandria Univ.†, Egypt-Japan Univ. of Sc. and Tech.‡, Carnegie Mellon Univ.*

Motivation

● Rendering touch input – user is wearing gloves.

● Wet/dirty hands.

Motivation

● Rendering touch input – user is wearing gloves.

● Wet/dirty hands.

● Special sensors● E.g. Samsung Galaxy S5

WiGest Approach

● Free the user from specialized sensors● Using the ubiquitous WiFi● Works with any phone

● Leveraging natural human movements to control devices

Basic Idea

• Leveraging the impact of hand motion on the received WiFion the phone to control WiFi-enabled devices.

WiGest Example

Challenges

• WiFi is a noisy signal• Avoiding false positives – human

interference• Handling the variations of

gestures and their attributes

• Energy consumption• No training

– Handle different humans– Same human a different times

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Agenda

Introduction• WiGest System• Results• Conlusions

WiGest System

Primitive Extraction

Primitive Extraction

1. Denoising– Using Wavelet denoising

2. Edges detection,– Extract different primitives.

3. Parameters extraction,– Magnitude: near/far.– Speed: fast/slow.

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Input Output

Noise Reduction

Time in sec

Scale

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Noise Reduction

• Removing noise using wavelet denoising– It can preserve signal details while filtering out the noise

and variations in the signal– Consists of three stages:

• Decomposition the signal to approximation and detail coefficients.

• Thresholding detail coefficients• Reconstruct the denoised signal by adding approximation and

detail after thresholding

Noise Reduction

Raw signal After denoising

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Edges Extraction

• Detecting gesture edges using wavelet analysis– In detail (high-pass filter) coefficients

• Falling edge causes a local maxima• Rising edge causes a local minima

Edges Extraction

Denoised signal

DWT detail coefficient

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Edges Extraction

Denoised signal


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Edges Extraction

Denoised signal


Edges Extraction

Denoised signal

DWT detail coefficientExtracted primitives

Parameters Extraction

Magnitude

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Far

Near

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Time in Seconds

Slow Fast

Speed

System Logic Flow

Segmentation

• Silence period determines the start and end of a gesture

• Preamble (unique signature), to start the communication channel

Frequency =

3 /2 Hz,

Count = 2

Frequency =

5 /2 Hz,

Count = 3

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RSSI

dBm

Time in SecondsSilence period Gesture delimiter

Pattern Encoding

• The extracted gesture primitives are converted to string sequence:

– Rising edge : +– Falling edge : -– Pause : 0

• Gesture matching– String matching to identify the gesture

Extracted gesture pattern : - + - +Gesture : Infinity

Gesture Identification

Extracted gesture pattern : - + - +Gesture : Infinity

Preamble • Energy efficiency• Human interference

and noise

Pattern encoding• Efficient matching• Error tolerance

Gestures ParametersFrequency =

3 /2 Hz,

Count = 2

Frequency =

5 /2 Hz,

Count = 3

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RSSI

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Time in Seconds

Count• Number of gesture

repetitions

• E.g. double click and single click

Frequency• Number of gesture

repetitions per unit time

• E.g. speed of basketball

dribble

System Logic Flow

Action Mapping

• At the end, the extracted gesture is mapped to an

application action .

Gesture Action

Application

Agenda

IntroductionWiGest System• Results• Conlusions

Implementation and Evaluation

• Test environments• Typical apartment• Engineering building at our campus

• More than 1000 experiment• Off-the-shelf WiFi-equipped devices• Hardware:

• Cisco Linksys

• Android-based cell phone.

• HP laptop

39 ft

34 ft

115 ft

49 ft

Experiments Performed

• Distance effect for different scenarios• Orientation• Number of APs• Human interference

• Whole home case study

• More results in the paper

Distance Impact

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Distance in feet

Accuracy Average RSSI

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Distance in feet


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Distance in feet


• 87% accuracy for distances up to 26ft

• Using a single AP

Orientation Impact

• Overall accuracy of 90.5%• The highest accuracy is in West – body not blocking the

signal

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Number of APs Impact

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Number of APs used in voting

• Accuracy 96% using 3 APs

• Reaches 100% with seven APs

Human interference

• Accuracy 89% in the presence of four

interfering humans

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Number of interfering humans

Whole Home Media-player Case Study

• Accuracy of 96% using 2 APs

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Act ion Classif ied

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34 ft

Agenda

IntroductionWiGest SystemResults• Conlusions

Conclusion

• WiGest is a gesture recognition system• Robust to noise in the environment and interfering humans• Does not require any training• Energy-efficient• Works with any WiFi-enabled device

• Primitives detection accuracy is 87.5%– Using a single AP

• Accuracy increases to 96% using three overheard APs.

Future Work

• Leveraging CSI for finer grained gestures• Other wireless technologies, e.g. cellular and

Bluetooth• Other applications

Thank You

Come and see our demo tomorrow10am to 1pm

wrc-ejust.org wrc_ejust wrc.ejust

wigest: a ubiquitous wifi-based gesture recognition system · wigest: a ubiquitous wifi-based...

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